Shared Address Space Computing: Programming Fork/Join
19/04/15
Shared Address Space Computing:
Programming
Alistair Rendell
See Chapter 6 or Lin and Synder, Chapter 7 of Grama,
Gupta, Karypis and Kumar, and Chapter 8 of Wilkinson
and Allen
Fork/Join Programming Model
Main Program
Spawned Processes
FORK
FORK
FORK
JOIN
JOIN
FORK
JOIN
JOIN
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(Heavyweight) UNIX Processes
• O/S like UNIX is based on the notion of a
process
– The CPU is shared between different
processes
• UNIX processes created via fork()
– Child copy an exact copy of parent, except it
has unique process ID
• Processes are “joined” using the system
calls wait()
– This introduces a synchronization
UNIX Fork Example
Pid = fork();
If (pid == 0){
code to be executed by slave
} else {
code to be executed by parent
}
If (pid == 0) exit (0); else wait(0);
• What is a zombie process?
• Why might fork be viewed as a heavyweight
operation?
– How is this cost reduced?
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Processes and Threads
Heap
IP
Code
Stack
A process
Interrupt
routines
Files
Stack
IP
Heap
Code
Stack
Two threads
Interrupt
routines
IP
Files
Why Threads
• Software Portability
– Applications can be developed on serial machine and run on
parallel machines without changes (is this really true?)
• Latency Hiding
– Ability to mask access to memory, I/O or communication by
having another thread execute in the meantime (but how quickly
can execution switch between threads?)
• Scheduling and Load Balancing
– For unstructured and dynamic applications (e.g. game playing)
loading balancing can be very hard. One option is to create more
threads than CPU resources and let the O/S sort out the
scheduling
• Ease of Programming, Widespread Use
– Due to above, threaded programs are easier to write (or develop
incrementally), so there has been widespread acceptance of
POSIX thread API (generally referred to as pthreads)
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Pthread Creation
main program
pthread create(
pthread_t *thread_handle,
const pthread_attr_t *attribute,
void * (*thread_function)(void *),
void *arg);
thread_function(arg)
{
pthread join(
pthread_t thread_handle,
void **status);
return status;
}
Threads and Threaded Code
• pthread_self() provides ID of calling routine
– pthread_equal(thread1, thread2) tests ID
– What is the analogous MPI call?
• Detached Threads
– Threads that will never synchronize via a join
– Specified via an attribute
– Why should we care?
– How do you handle this with UNIX fork?
• Re-entrant or thread-safe routines are those
than can be safely called when another instance
has been suspended in the middle of its
invocation
– Can you give an example of when a routine might not
be re-entrant?
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Example: Computing Pi
#include <pthread.h>
#include <stdlib.h>
#define MAX_THREADS 512
void *compute_pi (void *);
....
main() {
...
pthread_t p_threads[MAX_THREADS];
pthread_attr_t attr;
pthread_attr_init (&attr);
for (i=0; i< num_threads; i++) {
hits[i] = i;
pthread_create(&p_threads[i], &attr, compute_pi,
(void *) &hits[i]);
}
for (i=0; i< num_threads; i++) {
pthread_join(p_threads[i], NULL);
total_hits += hits[i];
}
...
}
Example: Computing Pi (cont)
void *compute_pi (void *s) {
int seed, i, *hit_pointer;
double rand_no_x, rand_no_y;
int local_hits;
hit_pointer = (int *) s;
seed = *hit_pointer;
local_hits = 0;
for (i = 0; i < sample_points_per_thread; i++) {
rand_no_x =(double)(rand_r(&seed))/(double)(RAND_MAX);
rand_no_y =(double)(rand_r(&seed))/(double)(RAND_MAX);
if (((rand_no_x - 0.5) * (rand_no_x - 0.5) +
(rand_no_y - 0.5) * (rand_no_y - 0.5)) < 0.25)
local_hits ++;
seed *= i;
}
*hit_pointer = local_hits;
pthread_exit(0);
}
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Programming and Performance
Notes
• Note the use of the function rand_r (instead of
superior random number generators such as
drand48).
• Executing this on a 4-processor SGI Origin, we
observe a 3.91 fold speedup at 32 threads. This
corresponds to a parallel efficiency of 0.98!
• We can also modify the program slightly to
observe the effect of false-sharing.
• The program can also be used to assess the
secondary cache line size.
Performance
• 4 processor SGI
Origin System using
upto 32 threads
• Instead of
incrementing
local_hits, we add to
a shared array using
stride of 1, 16 and
32
• (Taken from Grama,
Gupta, Karypis and
Kumar reference)
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Sharing Data
• Shared memory provides ability to share data
directly without having to pass messages
• For UNIX heavyweight processes additional
shared memory system calls are necessary
– Each process has its own virtual address space
– Shared memory system calls allow the processes to
“attach” to a segment of shared memory
– Use shmget() to allocate a shared memory segment,
shmat() to attach to it (either at a specific virtual
address or allow the O/S to return a suitable address)
• For threads, variables on the heap are shared
– What variables reside on the heap?
– What variables reside on the stack?
Accessing Shared Data
• Concurrent read is no problem
Time
– but what about concurrent write?
Instruction
P0
P1
x=x+1
read x
compute x+1
write x
read x
compute x+1
write x
• Similar problems with access to shared resources like I/O
• Critical Sections: mechanism to ensure controlled
access to shared resources
– Processes enter critical section under mutual exclusion
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Locks
• Simplest mechanism for providing mutual exclusion
• A lock is a 1-bit variable, a value of
– 1 indicates a process is in the critical section
– 0 indicates no process is in the critical section
• At its lowest level a lock is a protocol for coordinating
processes,
– the CPU is not physically prevented from executing those instruction
while (lock == 1) do_nothing;
lock = 1;
critical section
lock = 0;
/*infinite testing loop */
/*enter critical section*/
/*exit critical section*/
• The above is an incorrect example of a spin-lock, that uses
a mechanism called busy waiting
– What is wrong with the above?
– What low level hardware support is provided to address this?
Pthread Lock Routines
• Locks implemented as mutually exclusive lock
variables or mutex variables
• To use a variable must be declared as type
pthread_mutex_t
– Usually in main thread as it needs to be visible to all
threads using it
pthread_mutex_t mutex1;
pthread_mutex_init(&mutex1, NULL);
(NULL specifies default
attributes for mutex)
pthread_mutex_lock(&mutex1);
/* CRITICAL SECTION */
pthread_mutex_unlock(&mutex1);
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Deadly Embrace!
• Deadly embrace or deadlock can occur if two (or
more) processes are attempting to acquire two (or
more) resources that cannot be shared
– How does this relate to a group of dining philosophers?
R0
R1
Rn-1
Rn
P0
P1
Pn-1
Pn
n-process
deadlock
• Pthreads provides pthread_mutex_trylock()
to help address these circumstances
Semaphores#1
• Devised by Dijkstra (1968)
• A positive integer (including zero) operated on by two
operations named P (passeren) and V (vrijgeven)
– P(s) waits until s is greater than 0 and decrements s by 1 and
allows the process to continue
– V(s) increments s by 1 to release any one of the waiting
processes (if any)
– P and V operations are indivisible (atomic)
• In following s initialized to 1
P0
P1
P2
Noncritical section
Noncritical section
Noncritical section
P(s)
Critical Section
V(s)
P(s)
Critical Section
V(s)
P(s)
Critical Section
V(s)
Noncritical section
Noncritical section
Noncritical section
⋮
⋮
⋮
⋮
⋮
⋮
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Semaphores#2
• Binary semaphores only a value of 0 or 1
– hence act as a lock
• UNIX provides semaphores IPC (interprocedural communication) via
– semget() to get a semaphore array
– semtcl() to set initial value
– semop() to perform operations
• pthreads does not provide semaphores
– Easy to construct
– Available in real-time extensions
• What’s bad about semaphores?
Monitors (briefly)
• Hoare (1974): a higher level technique for
implementing mutual exclusion
– A suite of routines that provide the only method to
access a shared resource
– Essentially data and operations on that data are
encapsulated into one structure
• A monitor can only have one active instruction
pointer executing instructions from within the
monitor at any one time
– Why active?
• The concept of a monitor exists within Java
through the synchronized keyword
• Not available in pthreads or for UNIX processes
– A similar concept can be constructed
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Condition Variables#1
• A piece of code that needs to be executed when
a certain global condition exists
– E.g when a certain value of a variable is reached
• Using a lock would require frequent polling of the
lock value
– This would be busy waiting
• Condition variables define three operations
– wait(cond_var)
– signal(cond_var)
– status(cond_var)
-wait for condition to occur
-signal that condition occurred
-No of procs waiting on condition
• Wait will also release a lock or semaphore and
can be used to allow another process to alter the
condition
Condition Variable Example
• Consider example where process 0 will take some action
when the value of a global counter becomes zero
– In the following wait unlocks the lock and suspense the process
– The while statement will re-check the condition and is for good
error checking
Process 0
Other Processes
action()
{
⋮
lock();
while (x != 0)
wait(s);
unlock();
take_action();
⋮
}
counter()
{
⋮
lock();
x--;
if ( x == 0) signal(s);
unlock()
⋮
}
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Pthread Condition Variables
• Associated with a specific mutex
• Creation routines
pthread_cond_t cond1;
pthread_cond_init(&cond1; NULL);
pthread_cond_destroy();
• Waiting routines
pthread_cond_wait(cond1, mutex1);
pthread_cond_timedwait(cond1, mutex1, abstime);
• Signaling routines
pthread_cond_signal(cond1);
pthread_cond_broadcast(cond1);
– Signals are not remembered (what does this mean?)
Pthread CV Example
pthread_cond_t cond1;
pthread_mutex_t mutex1;
⋮
pthread_cond_init(&cond1, NULL);
pthread_mutex_init(&mutex1, NULL);
⋮
pthread_create()
⋮
Thread 0
Other Thread
action()
{
⋮
pthread_mutex_lock(&mutex1);
while (x != 0)
pthread_cond_wait(cond1,mutex1);
pthread_mutex_unlock(&mutex1);
take_action();
⋮
}
counter()
{
⋮
pthread_mutex_lock(&mutex1);
x--;
if ( x == 0)
pthread_cond_signal(cond1);
pthread_mutex_unlock(&mutex1)
⋮
}
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Barriers
• As with Message Passing, process/thread
synchronization often required
– Pthreads did not originally provide a native
barrier so it must be coded
• Now supported with functions
– pthread_barrier_init
– pthread_barrier_destroy
– pthread_barrier_wait
• Initialized with number of threads that are
required to be synchronized
Concurrent v Parallel Processing
• Concurrent Processing: Environment in which
tasks are defined and allowed to execute in any
order
– Does not imply a multiple processor environment
– Eg spawn a separate thread to do I/O while CPU
intensive task continues to do another operation
• Parallel Processing: The simultaneous
execution of concurrent tasks on different CPUs
All parallel processing is concurrent, but NOT all
concurrent processing is parallel
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O/S Thread Support
• O/S Originally designed for process not thread
support
• Require O/S to
– Treat threads equally and ensure that they all get
equal (or user defined) access to machine resources
– Know what to do when a thread issues a fork
command
– Be able to deliver a signal to the correct thread
• Libraries executed by a multi-threaded program
need to be thread safe
– Potential static data structures to be overwritten
• Hardware support
– Some hardware provide support for very fast context
switch between threads, e.g. Cray MTA or more
recently hyper-threading
Thread Implementations
• Three categories
– Pure user space
– Pure kernel space
– Mixed
• User Mode: When a process executes
instructions within its program or linked libraries
• Kernel Mode: When the operating system
executes instructions on behalf of a user
program
– Often as a result of a system call
– In kernel mode the program can access kernel space
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User Space Threads
Library scheduler
• No kernel involvement in
providing a thread
• Kernel has no knowledge
of threads and continues
to schedule processes
only
• Thread library selects
which thread to run
Library scheduler
Operating
System
Scheduler
Hardware
Running
CPU0
Running
CPU1
Runnable
Library scheduler
OK for concurrency, no
good for parallel
programming
Kernel Space Threads
Library scheduler
• Kernel level thread
created for each user
thread
• O/S must manage on a
per-thread basis
information typically
managed on a process
basis
• Potentially poor scaling
when too many threads
as O/S gets overloaded
Operating
System
Scheduler
Hardware
Running
CPU0
Runnable
Library scheduler
Running
CPU1
Runnable
Runnable
Runnable
Library scheduler
Runnable
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User-space v Kernel-space
• User Space Advantages
– No changes to core O/S
– No kernel involvement means they may be faster for
some applications
– Can create many threads without overloading the
system
• User Space Disadvantages
– Potentially long latency during system service
blocking (e.g. 1 thread stalled on I/O request)
– All threads compete for the same CPU cycles
– No advantage gained from multiple CPUs
• Mixed scheme
– A few user threads mapped to one kernel thread
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